AMINGA 2022 Survey

Libraries and Data

Lessons

Classes

Code

survey_df <- survey %>% 
  dplyr::select(
    sport_modalidade, 
    gender_genero, 
    english_1_not_great_3_neutral_5_amazing_ingles_1_suficiente_3_bom_5_muito_bom,
    art_1_not_great_3_neutral_5_amazing_arte_1_suficiente_3_bom_5_muito_bom,
    computer_1_not_great_3_neutral_5_amazing_computer_1_suficiente_3_bom_5_muito_bom,
    team_building_1_not_great_3_neutral_5_amazing_trabalho_de_equipa_1_suficiente_3_bom_5_muito_bom,
    yoga_mindfulness_1_not_great_3_neutral_5_amazing_ioga_atencao_plena_1_suficiente_3_bom_5_muito_bom,
    food_1_not_great_3_neutral_5_amazing_comida_1_suficiente_3_bom_5_muito_bom,
    aminga_staff_2022_overall_1_not_great_3_neutral_5_amazing_equipa_de_aminga_2022_em_geral_1_suficiente_3_bom_5_muito_bom,
    aminga_camp_2022_overall_1_not_great_3_neutral_5_amazing_campus_aminga_2022_em_geral_1_suficiente_3_bom_5_muito_bom
) %>% 
  dplyr::rename(
    english = english_1_not_great_3_neutral_5_amazing_ingles_1_suficiente_3_bom_5_muito_bom,
    art = art_1_not_great_3_neutral_5_amazing_arte_1_suficiente_3_bom_5_muito_bom,
    computer = computer_1_not_great_3_neutral_5_amazing_computer_1_suficiente_3_bom_5_muito_bom,
    team_building = team_building_1_not_great_3_neutral_5_amazing_trabalho_de_equipa_1_suficiente_3_bom_5_muito_bom,
    yoga = yoga_mindfulness_1_not_great_3_neutral_5_amazing_ioga_atencao_plena_1_suficiente_3_bom_5_muito_bom,
    food = food_1_not_great_3_neutral_5_amazing_comida_1_suficiente_3_bom_5_muito_bom,
    staff_overall = aminga_staff_2022_overall_1_not_great_3_neutral_5_amazing_equipa_de_aminga_2022_em_geral_1_suficiente_3_bom_5_muito_bom,
    camp_overall = aminga_camp_2022_overall_1_not_great_3_neutral_5_amazing_campus_aminga_2022_em_geral_1_suficiente_3_bom_5_muito_bom,
    sport = sport_modalidade,
    gender = gender_genero
  ) %>% 
  mutate(
    sport = trimws(gsub("\\([^\\(\\)]*\\)", "", sport)),
    gender = trimws(gsub("\\([^\\(\\)]*\\)", "", gender))
  ) %>% 
  mutate(across(where(is.numeric), function(x) factor(x, levels = 1:5, labels = c(
              "Not Great", 
              "Meh",
              "Neutral",
              "Great",
              "Amazing"
            ))))  %>% 
  as.data.frame()


comments_sport_df <- survey %>% 
  select(
    i_loved_about_the_sport_eu_amei_sobre_a_modalidade, 
    i_would_change_about_the_sport_eu_mudaria_sobre_a_modalidade,
    i_liked_about_the_sport_eu_gosto_sobre_a_modalidade,
    comments_regarding_art_class_comentarios_sobre_as_aulas_de_arte,
    comments_regarding_english_class_comentarios_sobre_a_aula_de_ingles,
    comments_regarding_computer_class_comentarios_sobre_as_aulas_de_informatica,
    comments_regarding_team_building_comentarios_sobre_trabalho_de_equipa,
    comments_regarding_yoga_class_comentarios_sobre_a_aula_de_ioga,
    comments_regarding_the_food_comentarios_sobre_a_comida,
    aminga_staff_2022_overall_insert_appreciations_for_any_of_the_staff_equipa_de_aminga_em_geral_algum_comentario_sobre_qualquer_um_da_equipa_aminga,
    any_comments_or_suggestions_algum_comentario_ou_sugestao
) 


prep_word_cloud <- function(colname){
  
  pt_stopwords <- tibble(word = stopwords::stopwords('pt'))
  
  comments_sport_df %>% 
    select({{colname}}) %>% 
    unnest_tokens(word, {{colname}}) %>% 
    count(word, sort = TRUE)%>% 
    anti_join(pt_stopwords)
  
}

Code

likert_subjects <- likert(items = survey_df %>% dplyr::select(english, art, computer, team_building, yoga))

Code

likert_subjects$results

We can see that for each of the subjects more than half of all students thought the class was amazing.

Code

summary(likert_subjects, center = 3, ordered = TRUE)

We find that at most 8% of students had a negative experience of any lesson.

Code

plot(likert_subjects, centered=TRUE, center=3, include.center=TRUE)

We can see that over 70% students had a positive experience of any of the lessons. In particular, 93% of students had a positive experience of Team Building.

Food

Code

likert_food <- likert(survey_df %>% select(food))
likert_food$results

We can see that more than three-quarters of students thought the food was amazing.

Code

summary(likert_food, center = 3, ordered = TRUE)

Code

plot(likert_food)

We find that 93% of students had a positive experience of the food.

Overall Camp

Code

likert_camp <- likert(survey_df %>% select(staff_overall, camp_overall))
likert_camp$results

Over 95% of students thought the staff were amazing and nearly 90% thought the camp overall was amazing.

Code

summary(likert_camp, center = 3, ordered = TRUE)

Nobody had a negative experience of either the camp nor the staff.

Code

plot(likert_camp, centered=TRUE, center=3, include.center=TRUE)

Everyone had a positive experience of the staff and 97% had a positive experience of the camp overall.

Return Next Year

Code

aminga_returns_counts <- survey %>% 
  select(do_you_want_to_return_to_aminga_camp_in_2023_queres_voltar_para_o_campus_aminga_em_2023) %>% 
  rename(return_2023 = do_you_want_to_return_to_aminga_camp_in_2023_queres_voltar_para_o_campus_aminga_em_2023) %>% 
  mutate(
    return_2023 = trimws(gsub("\\([^\\(\\)]*\\)", "", return_2023))
  ) %>% 
  count(return_2023) %>% 
  mutate(percentage = n/sum(n)) 

aminga_returns_counts %>%
  mutate(return_2023 = factor(return_2023, levels = c("No", "Maybe", "Yes"), labels = c("No", "Maybe", "Yes"), ordered = TRUE)) %>% 
  ggplot(., aes(return_2023, percentage)) + 
  geom_col() + 
  scale_y_continuous("Percentage\n", labels = percent) + 
  scale_x_discrete("\nResponse") + 
  ggtitle("Will you return to AMINGA in 2023?")

Gender

Classes

Code

temp_class_gender <- survey_df %>% 
  dplyr::select(gender, english, art, computer, team_building, yoga) %>% 
  drop_na()

likert_subjects_gender <- likert(
  items = temp_class_gender %>% dplyr::select(english, art, computer, team_building, yoga) , 
  grouping = temp_class_gender %>% dplyr::pull(gender)
)

likert_subjects_gender$results

Code

summary(likert_subjects_gender,center = 3, ordered = TRUE)

Code

plot(likert_subjects_gender, centered=TRUE, center=3, include.center=TRUE)

Food

Code

temp_food_gender <- survey_df %>% dplyr::select(gender, food) %>% drop_na()

likert_food_gender <- likert(temp_food_gender %>% select(food), grouping = temp_food_gender %>% pull(gender))
likert_food_gender$results

Code

summary(likert_food_gender, center = 3, ordered = TRUE)

Code

plot(likert_food_gender , centered=TRUE, center=3, include.center=TRUE)

Overall Camp

Code

temp_camp_gender <- survey_df %>% dplyr::select(gender, staff_overall, camp_overall) %>% drop_na()

likert_camp_gender <- likert(temp_camp_gender %>% select(staff_overall, camp_overall), grouping = temp_camp_gender %>% pull(gender))
likert_camp_gender$results

Code

summary(likert_camp_gender, center = 3, ordered = TRUE)

Code

plot(likert_camp_gender, centered=TRUE, center=3, include.center=TRUE)

Sport

Classes

Code

temp_class_sport <- survey_df %>% dplyr::select(sport, english, art, computer, team_building, yoga) %>% drop_na()

likert_subjects_sport <- likert(
  items = temp_class_sport %>% dplyr::select(english, art, computer, team_building, yoga) , 
  grouping = temp_class_sport %>% dplyr::pull(sport)
)

likert_subjects_sport$results

Code

summary(likert_subjects_sport,center = 3, ordered = TRUE)

Code

plot(likert_subjects_sport, centered=TRUE, center=3, include.center=TRUE)

Food

Code

temp_food_sport <- survey_df %>% dplyr::select(sport, food) %>% drop_na()

likert_food_sport <- likert(temp_food_sport %>% select(food), grouping = temp_food_sport %>% pull(sport))
likert_food_sport$results

Code

summary(likert_food_sport, center = 3, ordered = TRUE)

Code

plot(likert_food_sport , centered=TRUE, center=3, include.center=TRUE)

Overall Camp

Code

temp_overall_sport <- survey_df %>% dplyr::select(sport, staff_overall, camp_overall) %>% drop_na()

likert_camp_sport <- likert(temp_overall_sport %>% select(staff_overall, camp_overall), grouping = temp_overall_sport %>% pull(sport))
likert_camp_sport$results

Code

summary(likert_camp_sport, center = 3, ordered = TRUE)

Code

plot(likert_camp_sport, centered=TRUE, center=3, include.center=TRUE)

Sport By Gender

Classes

Code

temp_class_sport_gender<- survey_df %>% drop_na(gender, sport) %>% unite("sport group", c(gender, sport), sep = " ") %>% dplyr::select(`sport group`, english, art, computer, team_building, yoga) %>% drop_na()

likert_subjects_sport_gender <- likert(
  items = temp_class_sport_gender %>% dplyr::select(english, art, computer, team_building, yoga) , 
  grouping = temp_class_sport_gender %>% dplyr::pull(`sport group`)
)

likert_subjects_sport_gender$results

Code

summary(likert_subjects_sport_gender,center = 3, ordered = TRUE)

Code

plot(likert_subjects_sport_gender, centered=TRUE, center=3, include.center=TRUE)

Food

Code

temp_food_sport_gender <- survey_df  %>% drop_na(gender, sport) %>% unite("sport group", c(gender, sport), sep = " ") %>% dplyr::select(`sport group`, food) %>% drop_na()

likert_food_sport_gender <- likert(temp_food_sport_gender %>% select(food), grouping = temp_food_sport_gender %>% pull(`sport group`))
likert_food_sport_gender$results

Code

summary(likert_food_sport_gender, center = 3, ordered = TRUE)

Code

plot(likert_food_sport_gender , centered=TRUE, center=3, include.center=TRUE)

Overall Camp

Code

temp_overall_sport_gender <- survey_df %>% drop_na(gender, sport) %>% unite("sport group", c(gender, sport), sep = " ") %>% dplyr::select(`sport group`, staff_overall, camp_overall) %>% drop_na()

likert_camp_sport_gender <- likert(temp_overall_sport_gender %>% select(staff_overall, camp_overall), grouping = temp_overall_sport_gender %>% pull(`sport group`))
likert_camp_sport_gender$results

Code

summary(likert_camp_sport_gender, center = 3, ordered = TRUE)

Code

plot(likert_camp_sport_gender, centered=TRUE, center=3, include.center=TRUE)

Comments

Sports

I loved about the sport

Code

sport_love_count <- prep_word_cloud(i_loved_about_the_sport_eu_amei_sobre_a_modalidade) 
## Joining, by = "word"

wordcloud(words = sport_love_count$word, 
          freq = sport_love_count$n, 
          min.freq = 1, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

I would change about the sport

Code

sport_change_count <- prep_word_cloud(i_would_change_about_the_sport_eu_mudaria_sobre_a_modalidade) 
## Joining, by = "word"

wordcloud(words = sport_change_count$word, 
          freq = sport_change_count$n, 
          min.freq = 1, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

I liked about the sport

Code

sport_liked_count <- prep_word_cloud(i_liked_about_the_sport_eu_gosto_sobre_a_modalidade) 
## Joining, by = "word"

wordcloud(words = sport_liked_count$word, 
          freq = sport_liked_count$n, 
          min.freq = 1, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Arts Comments

Code

arts_comments <- prep_word_cloud(comments_regarding_art_class_comentarios_sobre_as_aulas_de_arte) 
## Joining, by = "word"

wordcloud(words = arts_comments$word, 
          freq = arts_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

English Comments

Code

english_comments <- prep_word_cloud(comments_regarding_english_class_comentarios_sobre_a_aula_de_ingles) 
## Joining, by = "word"

wordcloud(words = english_comments$word, 
          freq = english_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Computer Comments

Code

computer_comments <- prep_word_cloud(comments_regarding_computer_class_comentarios_sobre_as_aulas_de_informatica) 
## Joining, by = "word"

wordcloud(words = computer_comments$word, 
          freq = computer_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Team Building Comments

Code

team_building_comments <- prep_word_cloud(comments_regarding_team_building_comentarios_sobre_trabalho_de_equipa) 
## Joining, by = "word"

wordcloud(words = team_building_comments$word, 
          freq = team_building_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Yoga Comments

Code

yoga_comments <- prep_word_cloud(comments_regarding_yoga_class_comentarios_sobre_a_aula_de_ioga) 
## Joining, by = "word"

wordcloud(words = yoga_comments$word, 
          freq = yoga_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Food Comments

Code

food_comments <- prep_word_cloud(comments_regarding_the_food_comentarios_sobre_a_comida) 
## Joining, by = "word"

wordcloud(words = food_comments$word, 
          freq = food_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

AMINGA Staff Comments

Code

staff_comments <- prep_word_cloud(aminga_staff_2022_overall_insert_appreciations_for_any_of_the_staff_equipa_de_aminga_em_geral_algum_comentario_sobre_qualquer_um_da_equipa_aminga) 
## Joining, by = "word"

wordcloud(words = staff_comments$word, 
          freq = staff_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))

Any Comments or Suggestions

Code

any_comments <- prep_word_cloud(any_comments_or_suggestions_algum_comentario_ou_sugestao) 
## Joining, by = "word"

wordcloud(words = any_comments$word, 
          freq = any_comments$n, 
          min.freq = 2, 
          random.order=FALSE, 
          rot.per=0.3, 
          colors=brewer.pal(8, "Dark2"))